Upload PPO LunarLander-v2 trained agent
Browse files- README.md +1 -1
- config.json +1 -1
- ppo-LunarLander-v2.zip +2 -2
- ppo-LunarLander-v2/data +32 -32
- ppo-LunarLander-v2/policy.optimizer.pth +1 -1
- ppo-LunarLander-v2/policy.pth +1 -1
- ppo-LunarLander-v2/system_info.txt +5 -5
- results.json +1 -1
README.md
CHANGED
@@ -16,7 +16,7 @@ model-index:
|
|
16 |
type: LunarLander-v2
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
-
value:
|
20 |
name: mean_reward
|
21 |
verified: false
|
22 |
---
|
|
|
16 |
type: LunarLander-v2
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
+
value: 287.12 +/- 18.56
|
20 |
name: mean_reward
|
21 |
verified: false
|
22 |
---
|
config.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7cc39ac5d870>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7cc39ac5d900>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7cc39ac5d990>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7cc39ac5da20>", "_build": "<function ActorCriticPolicy._build at 0x7cc39ac5dab0>", "forward": "<function ActorCriticPolicy.forward at 0x7cc39ac5db40>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7cc39ac5dbd0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7cc39ac5dc60>", "_predict": "<function ActorCriticPolicy._predict at 0x7cc39ac5dcf0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7cc39ac5dd80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7cc39ac5de10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7cc39ac5dea0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7cc3a3df0840>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 5001304, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1692629405413708352, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAM1ut7yD4Bk/ql5HvVrAH79HBZi90J2VvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00026079999999994996, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 9884, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_shape": [8], "low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "_np_random": "Generator(PCG64)"}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "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", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": "Generator(PCG64)"}, "n_envs": 1, "n_steps": 2024, "gamma": 0.98, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 128, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
|
|
|
1 |
+
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ", "__init__": "<function ActorCriticPolicy.__init__ at 0x000001F74121B5E0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000001F74121B670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x000001F74121B700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x000001F74121B790>", "_build": "<function ActorCriticPolicy._build at 0x000001F74121B820>", "forward": "<function ActorCriticPolicy.forward at 0x000001F74121B8B0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x000001F74121B940>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x000001F74121B9D0>", "_predict": "<function ActorCriticPolicy._predict at 0x000001F74121BA60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x000001F74121BAF0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x000001F74121BB80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x000001F74121BC10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x000001F74121E600>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 2016000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1704447882488814100, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQgAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYACAAAAAAAAE3VjT75T4I+BSbavna6Eb9ExRw+w3WPvgAAAAAAAAAATdFsvYXj7rktN702jTUnsVMJWruDS921AACAPwAAgD+NRcW9TSEGP9P6EL6lIDe/JvE0vvNN7r0AAAAAAAAAANNGZD60dPu8voYwutDYvziZMlq+xcFmOQAAgD8AAIA/ZvL4Oyk4S7pjyxC0AklWrXBbRzpH4p8zAACAPwAAgD8mlG8+pzqJPxRsxD4wSiC/0IPOPiap4z0AAAAAAAAAAHpMSL7xCo0/NgW+vpNYK7+ko1S+9CUtvgAAAAAAAAAArX8cvqc5jz82Duq+tV0nvxZuTr6NXk6+AAAAAAAAAADmZp09fmuDPwv55D0U3GS/1FLePW82iD0AAAAAAAAAAObQdL3OH7o9v0afPdp7z76I7ri9tR2FPQAAAAAAAAAADX2sPc/cFj0yXFS+PH2pvgvaCr24NPO8AAAAAAAAAACGERS+jycNvAJIk7uKHye6TeVnPSLE8joAAIA/AACAP4B87L0hjIi8SKhBPiRrXL3rBBq9zn2uvgAAgD8AAIA/2lW8vaiNnz7VZ+U7aRsCvwoQgb3Hg5I9AAAAAAAAAADN2yU9B8WxPu5yub2SPBC/96uIPRf2Mb0AAAAAAAAAAJqUobz2OEm6J2WcvWPy9LJHkX67w2PXMwAAgD8AAIA/ZkGYPCnEXrrkIEq4/D8ys3N8Abvzym03AACAPwAAgD8zkx68OETEu8bpHLy//Xs8NaMxPV27WL0AAIA/AACAPzpqFb6PxCG8ROIjuwMq6blAnIg9G0+lOgAAgD8AAIA/APjWu1JonLmcJxe+/Vj3vX8kMbxHBbo+AACAPwAAAAAa7mO9eGHLPNG4Iz6GKC2+UmmWvNpnhz0AAAAAAAAAAAD4mbu4Ppu53cxkueQBWLRsZjg7FNKDOAAAgD8AAIA/ACr4POx5yrla0l20CfoDL9vxqTv324kzAACAPwAAgD+zS1y94eScumxWgrNQ424vYKKNuOnavjMAAIA/AACAP02XHT3U9LY/TmKgPnIuor0ekgC7dhTdPAAAAAAAAAAAzSypPOKLSD/WDKw9m/w7v0Rcpj3+3ae8AAAAAAAAAACT3wm+IvJ0P3is5r5/eV6/hx4jvrb5W74AAAAAAAAAAEAK4j23On4/CE1NPlLpS79ZFVg+CATOPQAAAAAAAAAAgLI0PQokMTw0Cyi+VeuavibMmb0QyVS8AAAAAAAAAAD9C5o+YQYdP4aKzr0Tp/++OxamPrL7Ob4AAAAAAAAAAMB75D1xqFu7GzdnvkefCL7h50q8tSsyPgAAgD8AAAAA5hCYPZ/E+ruO83K+NOwjvWViCT327Ac+AACAPwAAgD8NZra9JH42PFvXXz22ioy+riU5vYI9az0AAAAAAAAAAGbGnTsqgOY+q+jsOl9qR7/8sWE9XOy4vAAAAAAAAAAA+m42vpT4xj4BSxs94Pb8vrSbL77jShs+AAAAAAAAAADaY7i9PAW3P1tm376y4Va+t+kFvid3Ib4AAAAAAAAAAEaxdD5cba0/DUwaPxm/Br+yl5I+B0iZPgAAAAAAAAAAAAr6vBewtT9ahhi/uhy4PJcLgjw2Nw+8AAAAAAAAAAAAuS09XMMhuiIVir0UeQYyZXyQOxXX+7MAAIA/AACAP4Pgc77HyBs/E3oDPNIYAb+jsmq+e0QqPQAAAAAAAAAAAGGevVwLKLqgc988vxORvI7Ylbp1zH29AAAAAAAAgD/aIQ2+XJV7P0reEr4TiV6/abWivWtRX70AAAAAAAAAADMRGbwU6Ky62hqRt7Y/grIWwze5q1ymNgAAgD8AAIA/jXuXPaQ1Ubvgh7O9vFuVPMObpDw1n3+9AACAPwAAAABNIsI9yKGEO47MVL7g0gK+KA5KPAseGzwAAAAAAAAAAE058b3hXKO60E7qN0Yi4TMvzfq5zDkItwAAgD8AAIA/XceDPiLWBj9Uh8m9hlkJv8pamD5HyUC+AAAAAAAAAABmt2K99qxTun0FZDzZr0a2BloMus0EP7UAAIA/AACAP+bWXr2ZB4s/vj3XvfQkYr9jzfm8OBtavQAAAAAAAAAAsyQzvcyxrj8tIMG+G7KjvmRthLxok9e9AAAAAAAAAACFZ4a+eE2BPievsD6mJ76+qAzBvS4DET4AAAAAAAAAACASBj66lAY/4Wq/vKueI79Uwjw+3FqXvQAAAAAAAAAAcvaBvhrQLz9xApa+L6gnvysf1r4Y0em8AAAAAAAAAAATAys+61QMP24BKb6tEza/bQsUPlNd/L0AAAAAAAAAAG1XEL7F7S4/8446vvW1N7+IN22+CumQvQAAAAAAAAAATcfjvU08ND6jv5O8cHG8vrH5yb1m6Ei8AAAAAAAAAAAmqAK+Mp+yPxAu+r7Sf7i+2UQyvuPd6r0AAAAAAAAAAGY5sb4Q2HU/i4GavZybEr+nNLa+kjSZPQAAAAAAAAAAswAePdJLvbsuFgq+G7z9PDvUHj21w9K9AACAPwAAgD8z5IK8tBezPyv5xb7/Bi6+EDICPOvhpLwAAAAAAAAAAOYlQL3DY207TmZdPib4Qb4VNCO9ntTzPQAAAAAAAAAAmnujPK7FpbpnfyK+efktttdDGbldSJo1AACAPwAAgD8NRrM9hTPkueUHkbpuI4y3stA9uy1h3zcAAAAAAAAAAM2EN70xf64/D60evzGjw77ODHM8/Q9EvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYktASwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVswAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiS0CFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.008000000000000007, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 450, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_shape": [8], "low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 64, "n_steps": 700, "gamma": 0.99, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 32, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Windows-10-10.0.22000-SP0 10.0.22000", "Python": "3.9.13", "Stable-Baselines3": "2.0.0a5", "PyTorch": "1.13.1", "GPU Enabled": "True", "Numpy": "1.26.2", "Cloudpickle": "2.0.0", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
|
ppo-LunarLander-v2.zip
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3127ddd795c80f79aeb8c179e1106bf4c6bb8e0c74e9852d92f3b67c8f9475da
|
3 |
+
size 148610
|
ppo-LunarLander-v2/data
CHANGED
@@ -4,57 +4,57 @@
|
|
4 |
":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
|
5 |
"__module__": "stable_baselines3.common.policies",
|
6 |
"__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
|
7 |
-
"__init__": "<function ActorCriticPolicy.__init__ at
|
8 |
-
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at
|
9 |
-
"reset_noise": "<function ActorCriticPolicy.reset_noise at
|
10 |
-
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at
|
11 |
-
"_build": "<function ActorCriticPolicy._build at
|
12 |
-
"forward": "<function ActorCriticPolicy.forward at
|
13 |
-
"extract_features": "<function ActorCriticPolicy.extract_features at
|
14 |
-
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at
|
15 |
-
"_predict": "<function ActorCriticPolicy._predict at
|
16 |
-
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at
|
17 |
-
"get_distribution": "<function ActorCriticPolicy.get_distribution at
|
18 |
-
"predict_values": "<function ActorCriticPolicy.predict_values at
|
19 |
"__abstractmethods__": "frozenset()",
|
20 |
-
"_abc_impl": "<_abc._abc_data object at
|
21 |
},
|
22 |
"verbose": 1,
|
23 |
"policy_kwargs": {},
|
24 |
-
"num_timesteps":
|
25 |
-
"_total_timesteps":
|
26 |
"_num_timesteps_at_start": 0,
|
27 |
"seed": null,
|
28 |
"action_noise": null,
|
29 |
-
"start_time":
|
30 |
"learning_rate": 0.0003,
|
31 |
"tensorboard_log": null,
|
32 |
"_last_obs": {
|
33 |
":type:": "<class 'numpy.ndarray'>",
|
34 |
-
":serialized:": "
|
35 |
},
|
36 |
"_last_episode_starts": {
|
37 |
":type:": "<class 'numpy.ndarray'>",
|
38 |
-
":serialized:": "
|
39 |
},
|
40 |
"_last_original_obs": null,
|
41 |
"_episode_num": 0,
|
42 |
"use_sde": false,
|
43 |
"sde_sample_freq": -1,
|
44 |
-
"_current_progress_remaining": -0.
|
45 |
"_stats_window_size": 100,
|
46 |
"ep_info_buffer": {
|
47 |
":type:": "<class 'collections.deque'>",
|
48 |
-
":serialized:": "
|
49 |
},
|
50 |
"ep_success_buffer": {
|
51 |
":type:": "<class 'collections.deque'>",
|
52 |
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
53 |
},
|
54 |
-
"_n_updates":
|
55 |
"observation_space": {
|
56 |
":type:": "<class 'gymnasium.spaces.box.Box'>",
|
57 |
-
":serialized:": "
|
58 |
"dtype": "float32",
|
59 |
"bounded_below": "[ True True True True True True True True]",
|
60 |
"bounded_above": "[ True True True True True True True True]",
|
@@ -65,35 +65,35 @@
|
|
65 |
"high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
66 |
"low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
67 |
"high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
68 |
-
"_np_random":
|
69 |
},
|
70 |
"action_space": {
|
71 |
":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
|
72 |
-
":serialized:": "
|
73 |
"n": "4",
|
74 |
"start": "0",
|
75 |
"_shape": [],
|
76 |
"dtype": "int64",
|
77 |
-
"_np_random":
|
78 |
},
|
79 |
-
"n_envs":
|
80 |
-
"n_steps":
|
81 |
-
"gamma": 0.
|
82 |
"gae_lambda": 0.98,
|
83 |
"ent_coef": 0.01,
|
84 |
"vf_coef": 0.5,
|
85 |
"max_grad_norm": 0.5,
|
86 |
-
"batch_size":
|
87 |
-
"n_epochs":
|
88 |
"clip_range": {
|
89 |
":type:": "<class 'function'>",
|
90 |
-
":serialized:": "
|
91 |
},
|
92 |
"clip_range_vf": null,
|
93 |
"normalize_advantage": true,
|
94 |
"target_kl": null,
|
95 |
"lr_schedule": {
|
96 |
":type:": "<class 'function'>",
|
97 |
-
":serialized:": "
|
98 |
}
|
99 |
}
|
|
|
4 |
":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
|
5 |
"__module__": "stable_baselines3.common.policies",
|
6 |
"__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
|
7 |
+
"__init__": "<function ActorCriticPolicy.__init__ at 0x000001F74121B5E0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000001F74121B670>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x000001F74121B700>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x000001F74121B790>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x000001F74121B820>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x000001F74121B8B0>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x000001F74121B940>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x000001F74121B9D0>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x000001F74121BA60>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x000001F74121BAF0>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x000001F74121BB80>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x000001F74121BC10>",
|
19 |
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x000001F74121E600>"
|
21 |
},
|
22 |
"verbose": 1,
|
23 |
"policy_kwargs": {},
|
24 |
+
"num_timesteps": 2016000,
|
25 |
+
"_total_timesteps": 2000000,
|
26 |
"_num_timesteps_at_start": 0,
|
27 |
"seed": null,
|
28 |
"action_noise": null,
|
29 |
+
"start_time": 1704447882488814100,
|
30 |
"learning_rate": 0.0003,
|
31 |
"tensorboard_log": null,
|
32 |
"_last_obs": {
|
33 |
":type:": "<class 'numpy.ndarray'>",
|
34 |
+
":serialized:": "gAWVdQgAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYACAAAAAAAAE3VjT75T4I+BSbavna6Eb9ExRw+w3WPvgAAAAAAAAAATdFsvYXj7rktN702jTUnsVMJWruDS921AACAPwAAgD+NRcW9TSEGP9P6EL6lIDe/JvE0vvNN7r0AAAAAAAAAANNGZD60dPu8voYwutDYvziZMlq+xcFmOQAAgD8AAIA/ZvL4Oyk4S7pjyxC0AklWrXBbRzpH4p8zAACAPwAAgD8mlG8+pzqJPxRsxD4wSiC/0IPOPiap4z0AAAAAAAAAAHpMSL7xCo0/NgW+vpNYK7+ko1S+9CUtvgAAAAAAAAAArX8cvqc5jz82Duq+tV0nvxZuTr6NXk6+AAAAAAAAAADmZp09fmuDPwv55D0U3GS/1FLePW82iD0AAAAAAAAAAObQdL3OH7o9v0afPdp7z76I7ri9tR2FPQAAAAAAAAAADX2sPc/cFj0yXFS+PH2pvgvaCr24NPO8AAAAAAAAAACGERS+jycNvAJIk7uKHye6TeVnPSLE8joAAIA/AACAP4B87L0hjIi8SKhBPiRrXL3rBBq9zn2uvgAAgD8AAIA/2lW8vaiNnz7VZ+U7aRsCvwoQgb3Hg5I9AAAAAAAAAADN2yU9B8WxPu5yub2SPBC/96uIPRf2Mb0AAAAAAAAAAJqUobz2OEm6J2WcvWPy9LJHkX67w2PXMwAAgD8AAIA/ZkGYPCnEXrrkIEq4/D8ys3N8Abvzym03AACAPwAAgD8zkx68OETEu8bpHLy//Xs8NaMxPV27WL0AAIA/AACAPzpqFb6PxCG8ROIjuwMq6blAnIg9G0+lOgAAgD8AAIA/APjWu1JonLmcJxe+/Vj3vX8kMbxHBbo+AACAPwAAAAAa7mO9eGHLPNG4Iz6GKC2+UmmWvNpnhz0AAAAAAAAAAAD4mbu4Ppu53cxkueQBWLRsZjg7FNKDOAAAgD8AAIA/ACr4POx5yrla0l20CfoDL9vxqTv324kzAACAPwAAgD+zS1y94eScumxWgrNQ424vYKKNuOnavjMAAIA/AACAP02XHT3U9LY/TmKgPnIuor0ekgC7dhTdPAAAAAAAAAAAzSypPOKLSD/WDKw9m/w7v0Rcpj3+3ae8AAAAAAAAAACT3wm+IvJ0P3is5r5/eV6/hx4jvrb5W74AAAAAAAAAAEAK4j23On4/CE1NPlLpS79ZFVg+CATOPQAAAAAAAAAAgLI0PQokMTw0Cyi+VeuavibMmb0QyVS8AAAAAAAAAAD9C5o+YQYdP4aKzr0Tp/++OxamPrL7Ob4AAAAAAAAAAMB75D1xqFu7GzdnvkefCL7h50q8tSsyPgAAgD8AAAAA5hCYPZ/E+ruO83K+NOwjvWViCT327Ac+AACAPwAAgD8NZra9JH42PFvXXz22ioy+riU5vYI9az0AAAAAAAAAAGbGnTsqgOY+q+jsOl9qR7/8sWE9XOy4vAAAAAAAAAAA+m42vpT4xj4BSxs94Pb8vrSbL77jShs+AAAAAAAAAADaY7i9PAW3P1tm376y4Va+t+kFvid3Ib4AAAAAAAAAAEaxdD5cba0/DUwaPxm/Br+yl5I+B0iZPgAAAAAAAAAAAAr6vBewtT9ahhi/uhy4PJcLgjw2Nw+8AAAAAAAAAAAAuS09XMMhuiIVir0UeQYyZXyQOxXX+7MAAIA/AACAP4Pgc77HyBs/E3oDPNIYAb+jsmq+e0QqPQAAAAAAAAAAAGGevVwLKLqgc988vxORvI7Ylbp1zH29AAAAAAAAgD/aIQ2+XJV7P0reEr4TiV6/abWivWtRX70AAAAAAAAAADMRGbwU6Ky62hqRt7Y/grIWwze5q1ymNgAAgD8AAIA/jXuXPaQ1Ubvgh7O9vFuVPMObpDw1n3+9AACAPwAAAABNIsI9yKGEO47MVL7g0gK+KA5KPAseGzwAAAAAAAAAAE058b3hXKO60E7qN0Yi4TMvzfq5zDkItwAAgD8AAIA/XceDPiLWBj9Uh8m9hlkJv8pamD5HyUC+AAAAAAAAAABmt2K99qxTun0FZDzZr0a2BloMus0EP7UAAIA/AACAP+bWXr2ZB4s/vj3XvfQkYr9jzfm8OBtavQAAAAAAAAAAsyQzvcyxrj8tIMG+G7KjvmRthLxok9e9AAAAAAAAAACFZ4a+eE2BPievsD6mJ76+qAzBvS4DET4AAAAAAAAAACASBj66lAY/4Wq/vKueI79Uwjw+3FqXvQAAAAAAAAAAcvaBvhrQLz9xApa+L6gnvysf1r4Y0em8AAAAAAAAAAATAys+61QMP24BKb6tEza/bQsUPlNd/L0AAAAAAAAAAG1XEL7F7S4/8446vvW1N7+IN22+CumQvQAAAAAAAAAATcfjvU08ND6jv5O8cHG8vrH5yb1m6Ei8AAAAAAAAAAAmqAK+Mp+yPxAu+r7Sf7i+2UQyvuPd6r0AAAAAAAAAAGY5sb4Q2HU/i4GavZybEr+nNLa+kjSZPQAAAAAAAAAAswAePdJLvbsuFgq+G7z9PDvUHj21w9K9AACAPwAAgD8z5IK8tBezPyv5xb7/Bi6+EDICPOvhpLwAAAAAAAAAAOYlQL3DY207TmZdPib4Qb4VNCO9ntTzPQAAAAAAAAAAmnujPK7FpbpnfyK+efktttdDGbldSJo1AACAPwAAgD8NRrM9hTPkueUHkbpuI4y3stA9uy1h3zcAAAAAAAAAAM2EN70xf64/D60evzGjw77ODHM8/Q9EvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYktASwiGlIwBQ5R0lFKULg=="
|
35 |
},
|
36 |
"_last_episode_starts": {
|
37 |
":type:": "<class 'numpy.ndarray'>",
|
38 |
+
":serialized:": "gAWVswAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiS0CFlIwBQ5R0lFKULg=="
|
39 |
},
|
40 |
"_last_original_obs": null,
|
41 |
"_episode_num": 0,
|
42 |
"use_sde": false,
|
43 |
"sde_sample_freq": -1,
|
44 |
+
"_current_progress_remaining": -0.008000000000000007,
|
45 |
"_stats_window_size": 100,
|
46 |
"ep_info_buffer": {
|
47 |
":type:": "<class 'collections.deque'>",
|
48 |
+
":serialized:": "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"
|
49 |
},
|
50 |
"ep_success_buffer": {
|
51 |
":type:": "<class 'collections.deque'>",
|
52 |
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
53 |
},
|
54 |
+
"_n_updates": 450,
|
55 |
"observation_space": {
|
56 |
":type:": "<class 'gymnasium.spaces.box.Box'>",
|
57 |
+
":serialized:": "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",
|
58 |
"dtype": "float32",
|
59 |
"bounded_below": "[ True True True True True True True True]",
|
60 |
"bounded_above": "[ True True True True True True True True]",
|
|
|
65 |
"high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
66 |
"low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
67 |
"high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
68 |
+
"_np_random": null
|
69 |
},
|
70 |
"action_space": {
|
71 |
":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
|
72 |
+
":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
|
73 |
"n": "4",
|
74 |
"start": "0",
|
75 |
"_shape": [],
|
76 |
"dtype": "int64",
|
77 |
+
"_np_random": null
|
78 |
},
|
79 |
+
"n_envs": 64,
|
80 |
+
"n_steps": 700,
|
81 |
+
"gamma": 0.99,
|
82 |
"gae_lambda": 0.98,
|
83 |
"ent_coef": 0.01,
|
84 |
"vf_coef": 0.5,
|
85 |
"max_grad_norm": 0.5,
|
86 |
+
"batch_size": 32,
|
87 |
+
"n_epochs": 10,
|
88 |
"clip_range": {
|
89 |
":type:": "<class 'function'>",
|
90 |
+
":serialized:": "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"
|
91 |
},
|
92 |
"clip_range_vf": null,
|
93 |
"normalize_advantage": true,
|
94 |
"target_kl": null,
|
95 |
"lr_schedule": {
|
96 |
":type:": "<class 'function'>",
|
97 |
+
":serialized:": "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"
|
98 |
}
|
99 |
}
|
ppo-LunarLander-v2/policy.optimizer.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 87929
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:743bd2100f855e15fbc0bca9a11dd7e8457d02203951697e84a41b7af2aaa23a
|
3 |
size 87929
|
ppo-LunarLander-v2/policy.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 43329
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:bfc6057e7255b3373b62cb878a33dbbe68582b9f9ea41ccdf3596c82159e37ac
|
3 |
size 43329
|
ppo-LunarLander-v2/system_info.txt
CHANGED
@@ -1,9 +1,9 @@
|
|
1 |
-
- OS:
|
2 |
-
- Python: 3.
|
3 |
- Stable-Baselines3: 2.0.0a5
|
4 |
-
- PyTorch:
|
5 |
- GPU Enabled: True
|
6 |
-
- Numpy: 1.
|
7 |
-
- Cloudpickle: 2.
|
8 |
- Gymnasium: 0.28.1
|
9 |
- OpenAI Gym: 0.25.2
|
|
|
1 |
+
- OS: Windows-10-10.0.22000-SP0 10.0.22000
|
2 |
+
- Python: 3.9.13
|
3 |
- Stable-Baselines3: 2.0.0a5
|
4 |
+
- PyTorch: 1.13.1
|
5 |
- GPU Enabled: True
|
6 |
+
- Numpy: 1.26.2
|
7 |
+
- Cloudpickle: 2.0.0
|
8 |
- Gymnasium: 0.28.1
|
9 |
- OpenAI Gym: 0.25.2
|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"mean_reward":
|
|
|
1 |
+
{"mean_reward": 287.1197589506502, "std_reward": 18.562195803458927, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-01-05T13:43:48.000952"}
|